Business Description

In a marketplace of rapid advancement of Artificial Intelligence (AI), SMC identifies multiple opportunities within the FinTech universe to pursue for its' shareholders. The Company sees itself as a FinTech-Disruptor, seeking businesses that are pioneering the digital world and investing in AI, specifically within the financial services domain. Artificial Intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the expert human mind. We continue to develop Fyniti Global Equities EBT Index and have integrated this technology into our FYNN AI platform. Fyniti, a fintech platform developer, was founded by veteran Wall Street technologists and investment bankers with experience at Goldman Sachs, JP Morgan Chase, Bank of America (Merrill Lynch), and Citigroup. The Company has a clear focus on developing disruptive technologies for the wealth management and capital markets sectors. Fyniti owns the IQ Engine and EBT Technology, which together provide AI-driven quantitative investing and AI-based basket trading. While originally intended to generate a standalone revenue stream, we have now combined all legacy platforms into one cohesive platform. The Platform’s IQ Engine enables the user to see changes occurring on the underlying indexes daily and provides alternatives based on machine learning (“ML”) and artificial intelligence (“AI”) which the user can incorporate into its decision making process. Our AI and ML Capabilities: Fyniti Global Equities EBT employs state-of-the-art AI/ML technologies along with Quant algorithms, to enhance our quantitative investing strategies and wealth management solutions. The primary purpose of our AI/ML capabilities is to optimize trading strategies, risk management, and portfolio allocation. Operation of the IQ Engine: The IQ Engine operates by continuously analyzing vast amounts of financial data, market indicators, and historical trading patterns. It employs advanced statistical models and machine learning algorithms to identify trends, correlations, and anomalies in the data. These insights are then used to make data-driven decisions regarding the execution of trades, asset allocation, and risk mitigation. By using the IQ engine, Financial Institutions, RIAs, and CPAs will have more access to information flow with which to make better decisions for their clients. Datasets Utilized: Our AI/ML algorithms utilize a wide range of datasets to inform their decision-making processes. These datasets include but are not limited to: We use both public and paid sources for input. Most of the input is correlated by amassing the collective data points at the end of business everyday. Our AI/ML algorithms utilize a wide range of datasets to inform their decision-making processes. The AL/ML Algorithm captures between 20 and 30 thousand data points every day from different places. These data points comprise but are not limited to: Market Data: Real-time and historical price data, trading volumes, bid-ask spreads, and order book information. Economic Indicators: Macro-economic data such as GDP, inflation rates, and interest rates. News and Sentiment Analysis: News articles, social media sentiment, and other textual data sources to gauge market sentiment. Fundamental Data: Company financials, earnings reports, and analyst recommendations. Alternative Data: Non-traditional data sources are also incorporated to enhance the capabilities. As an example, regarding the financial sector. Buy now pay later tracking, Credit card usage, M1distribution, and Fed Data. We obtain those datasets from paid data providers like financial data publishers as well as public sources like corporate SEC filings Edgar and Bloomberg etc. The nature of these datasets is both structured and unstructured data. Depending on the source of the dataset, the cost varies. Regarding the AI, we use 3 broader types of algorithms, supervised learning, unsupervised learning and reinforcement learning. The SaaS is used to create a projected weighting on each block. In essence creating a customized index basket that is actionable on by the RIA. The institution would have access to alerts on a daily basis showing recommended adjustments within their customized block.…